Metadata impact on research paper similarity

While collaborative filtering and citation analysis have been well studied for research paper recommender systems, content-based approaches typically restrict themselves to straightforward application of the vector space model. However, various types of metadata containing potentially useful information are usually available as well. Our work explores several methods to exploit this information in combination with different similarity measures

Rethinking Consistency Management in Real-time Collaborative Editing Systems

Networked computer systems offer much to support collaborative editing of shared documents among users. Increasing concurrent access to shared documents by allowing multiple users to contribute to and/or track changes to these shared documents is the goal of real-time collaborative editing systems (RTCES); yet concurrent access is either limited in existing systems that employ exclusive locking or concurrency control algorithms such as operational transformation (OT) may be employed to enable concurrent access. Unfortunately, such OT based schemes are costly with respect to communication and computation. Further, existing systems are often specialized in their functionality and require users to adopt new, unfamiliar software to enable collaboration. This research discusses our work in improving consistency management in RTCES. We have developed a set of deadlock-free multi-granular dynamic locking algorithms and data structures that maximize concurrent access to shared documents while minimizing communication cost. These algorithms provide a high level of service for concurrent access to the shared document and integrate merge-based or OT-based consistency maintenance policies locally among a subset of the users within a subsection of the document – thus reducing the communication costs in maintaining consistency. Additionally, we have developed client-server and P2P implementations of our hierarchical document management algorithms. Simulations results indicate that our approach achieves significant communication and computation cost savings. We have also developed a hierarchical reduction algorithm that can minimize the space required of RTCES, and this algorithm may be pipelined through our document tree. Further, we have developed an architecture that allows for a heterogeneous set of client editing software to connect with a heterogeneous set of server document repositories via Web services. This architecture supports our algorithms and does not require client or server technologies to be modified – thus it is able to accommodate existing, favored editing and repository tools. Finally, we have developed a prototype benchmark system of our architecture that is responsive to users’ actions and minimizes communication costs

Twitter in Academic Conferences: Usage, Networking and Participation over Time

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

Ontologies Supporting Intelligent Agent-Based Assistance

Intelligent agent-based assistants are systems that try to simplify peoples work based on computers. Recent research on intelligent assistance has presented significant results in several and different situations. Building such a system is a difficult task that requires expertise in numerous artificial intelligence and engineering disciplines. A key point in this kind of system is knowledge handling. The use of ontologies for representing domain knowledge and for supporting reasoning is becoming wide-spread in many areas, including intelligent assistance. In this paper we present how ontologies can be used to support intelligent assistance in a multi-agent system context. We show how ontologies may be spread over the multi-agent system architecture, highlighting their role controlling user interaction and service description. We present in detail an ontology-based conversational interface for personal assistants, showing how to design an ontology for semantic interpretation and how the interpretation process uses it for semantic analysis. We also present how ontologies are used to describe decentralized services based on a multi-agent architecture